Short-Term Load Forecasting Method for Renewable Energy Integration and Grid Stability Using CNN, LSTM, and Transformer Models

Abstract

This study examines the feasibility of combining Morocco's renewable energy plan with artificial intelligence to improve energy management in the industrial sector. Based on Moroccan Law 82-21, which promotes the self-consumption of renewable energy, the study addresses the fundamental difficulty of accurately estimating energy consumption in dynamic industrial environments. This difficulty is addressed using advanced machine learning models such as convolutional neural networks (CNNs), long-term memory networks (LSTMs) and transformers. The results show that deep learning models outperform classical methods such as ARIMA, with transformers and LSTM models excelling at handling erratic and steady energy consumption patterns, respectively. In particular, hybrid CNN-LSTM architectures provide the highest level of accuracy, with prediction accuracy improved by up to 20\%. While improving grid stability and renewable energy integration, this development has the potential to reduce operational costs by up to 30\%. This analysis not only supports Morocco's ambitious goal of generating 52\% of its electricity from renewables by 2030 but also highlights the critical role of AI-based solutions in creating a sustainable energy future.